CN106874907A - A kind of method and device for setting up Car license recognition model - Google Patents
A kind of method and device for setting up Car license recognition model Download PDFInfo
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- CN106874907A CN106874907A CN201710039025.6A CN201710039025A CN106874907A CN 106874907 A CN106874907 A CN 106874907A CN 201710039025 A CN201710039025 A CN 201710039025A CN 106874907 A CN106874907 A CN 106874907A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/63—Scene text, e.g. street names
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F18/24—Classification techniques
Abstract
A kind of method and device for setting up Car license recognition model, wherein, the method for setting up Car license recognition model, including：Obtain the license plate image of multiple vehicles；The license plate image is expanded to pre-set dimension, license plate image sample is obtained；Using the license board information in the multiple license plate image sample and the license plate image sample as training data, neural network model is trained, until the neural network model converges on preset value to the discrimination of the license plate image sample license board information more than the penalty values of predetermined threshold value or the loss function of neural network model, then license board information identification is carried out to license plate image to be identified using the neural network model for training, avoid cumbersome License Plate Segmentation process, solve the problems, such as existing licence plate recognition method do not adapt to environment complicated and changeable and identification process take it is serious.
Description
Technical field
The present invention relates to image identification technical field, and in particular to a kind of method and device for setting up Car license recognition model.
Background technology
In intelligent transportation field, license plate recognition technology is in occupation of consequence.Traditional license plate recognition technology typically will
Car license recognition is divided into several big modules such as License Plate, License Plate Character Segmentation, Recognition of License Plate Characters.Existing licence plate recognition method be by
Into some single characters, the single character after then Recognition of License Plate Characters is to cutting is carried out car plate picture Character segmentation after positioning
Identification classification.
But existing licence plate recognition method have the shortcomings that it is certain, for example, occur being stained when car plate, incomplete, fracture, adhesion
When, traditional character segmentation method faces huge challenge, and segmentation accuracy drastically declines, and directly results in Car license recognition mistake
Lose.Therefore, the single character after traditional Car license recognition does not adapt to environment complicated and changeable, and Car license recognition process to cutting
Classification is identified, a large amount of digital image-processing methods are taken, taken serious.
The content of the invention
Therefore, the technical problem to be solved in the present invention is that existing licence plate recognition method does not adapt to environment complicated and changeable
And identification process is time-consuming serious.
In view of this, the present invention provides a kind of method for setting up Car license recognition model, including：
Obtain the license plate image of multiple vehicles；
The license plate image is expanded to pre-set dimension, license plate image sample is obtained；
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to god
It is trained through network model, until the neural network model is more than to the discrimination of the license plate image sample license board information
The penalty values of the loss function of the first predetermined threshold value or neural network model converge on preset value.
Preferably, the neural network model is convolutional neural networks model.
Preferably, the neural network model is provided with multiple character result output layers, the multiple character result output
Layer corresponds to the recognition result of multiple characters in the output license plate image sample respectively；
The license board information using in the multiple license plate image sample and the license plate image sample as training data,
Neural network model is trained, until the neural network model is big to the discrimination of license board information in license plate image sample
Preset value is converged in the penalty values of predetermined threshold value or the loss function of neural network model, including：
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to god
It is trained through network model, until multiple character result output layers of the neural network model are to more in license plate image sample
The discrimination of the recognition result of individual character is converged on more than the penalty values of predetermined threshold value or the loss function of neural network model
Preset value.
Preferably, the neural network model is provided with length sequences output layer, for exporting the license plate image sample
Middle character length recognition result.
Preferably, it is described that the license plate image is expanded to pre-set dimension, license plate image sample is obtained, including：
Obtain the coordinate and size of the license plate image；
According to the coordinate and size of the license plate image, the image center of the license plate image is determined；
Centered on described image central point, the license plate image extension is carried out according to the pre-set dimension, obtain described
License plate image sample.
Correspondingly, the present invention also provides a kind of device for setting up Car license recognition model, including：
Acquiring unit, the license plate image for obtaining multiple vehicles；
License plate image sample acquisition unit, for the license plate image to be expanded into pre-set dimension, obtains license plate image sample
This；
Training unit, for using the license board information in the multiple license plate image sample and the license plate image sample as
Training data, is trained to neural network model, until the neural network model is to car plate in the license plate image sample
The discrimination of information converges on preset value more than the penalty values of the first predetermined threshold value or the loss function of neural network model.
Preferably, the neural network model is convolutional neural networks model.
Preferably, the neural network model is provided with multiple character result output layers, the multiple character result output
Layer corresponds to the recognition result of multiple characters in the output license plate image sample respectively；
The training unit, including：Training subelement, is used for
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to god
It is trained through network model, until multiple character result output layers of the neural network model are to more in license plate image sample
The discrimination of the recognition result of individual character is converged on more than the penalty values of predetermined threshold value or the loss function of neural network model
Preset value.
Preferably, the neural network model is provided with length sequences output layer, for exporting the license plate image sample
Middle character length recognition result.
Preferably, the license plate image sample acquisition unit includes：
Coordinate and size acquiring unit, coordinate and size for obtaining the license plate image；
Image center determining unit, for coordinate and size according to the license plate image, determines the car plate
The image center of image；
License plate image sample determining unit, for centered on described image central point, being carried out according to the pre-set dimension
The license plate image extension, obtains the license plate image sample.
Technical solution of the present invention has advantages below：
By obtaining the license plate image of multiple vehicles, license plate image is expanded to pre-set dimension, obtains license plate image sample,
And using the license board information in multiple license plate image samples and license plate image sample as training data, neural network model is carried out
Training, until neural network model is more than the first predetermined threshold value or nerve net to the discrimination of license plate image sample license board information
The penalty values of the loss function of network model converge on preset value, then using the neural network model for training to car plate to be identified
Image carries out license board information identification, solves existing licence plate recognition method and does not adapt to environment complicated and changeable and identification process consumption
The problem of Shi Yanchong.
Brief description of the drawings
In order to illustrate more clearly of the specific embodiment of the invention or technical scheme of the prior art, below will be to specific
The accompanying drawing to be used needed for implementation method or description of the prior art is briefly described, it should be apparent that, in describing below
Accompanying drawing is some embodiments of the present invention, for those of ordinary skill in the art, before creative work is not paid
Put, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of method for setting up Car license recognition model that the embodiment of the present invention 1 is provided；
Fig. 2 is a kind of structural representation of device for setting up Car license recognition model that the embodiment of the present invention 2 is provided；
Fig. 3 is a kind of flow chart of license board information recognition methods that the embodiment of the present invention 3 is provided；
Fig. 4 is a kind of structural representation of license board information identifying device that the embodiment of the present invention 4 is provided.
Specific embodiment
Technical scheme is clearly and completely described below in conjunction with accompanying drawing, it is clear that described implementation
Example is a part of embodiment of the invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill
The every other embodiment that personnel are obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Embodiment 1
The embodiment of the present invention provides a kind of method for setting up Car license recognition model, as shown in figure 1, including：
S11, obtains the license plate image of multiple vehicles.Plurality of license plate image sample includes positive sample and negative sample, just
Sample be true license plate image, negative sample be non-genuine license plate image, i.e. negative sample can be other positions of vehicle image or
The incomplete license plate image of person, is labeled to Massive Sample, and marked content includes the number-plate number and/or characters on license plate length, false
The all characters of board are all labeled as 0.All of sample is finally zoomed to the input layer size of neural network model, the present embodiment god
It is 128x128 through the size of network model input layer.The acquisition of license plate image carries out License Plate using ACF detection algorithms.
S12, pre-set dimension is expanded to by license plate image, obtains license plate image sample.The car plate that ACF detection algorithms are detected
The not small probability event situation comprising complete license plate area is there may be, so that license board information identification is inaccurate, in order to
Training result accuracy is improved, the license plate image region of acquisition is amplified, specifically include following steps：
S121, obtains the coordinate and size of license plate image；License plate image is wherein obtained by ACF detection algorithms, profit
The coordinate and picture size size of license plate image are obtained with minimum enclosed rectangle method.
S122, according to the coordinate and size of license plate image, determines the image center of license plate image；
S123, centered on image center, license plate image extension is carried out according to pre-set dimension, obtains license plate image sample
This.The size of wherein described license plate image sample is more than license plate image size, and pre-set dimension can be the license plate image for obtaining
Maximum in length and width, or according to obtaining the actual size statistics of license plate image as propagation size, wherein
Statistics can be the average value of random license plate image sized data.
S13, using the license board information in multiple license plate image samples and license plate image sample as training data, to nerve net
Network model is trained, until neural network model is more than the first predetermined threshold value to the discrimination of license plate image sample license board information
Or the penalty values of the loss function of neural network model converge on preset value.The penalty values are used to calculate neural network model
Convergence rate, when the penalty values of loss function change in the certain limit of preset value, you can think loss function value not
Decline again, wherein neural network model is convolutional neural networks model, the license plate image sample that will be obtained is input to input layer, pass through
If crossing dried layer convolutional layer and pond layer, the embodiment of the present invention uses 4 layers of convolutional layer and 4 layers of pond layer alternating action, obtains one
The characteristic vector H of 4096 dimensions.The convolution kernel of all of convolutional layer is 3, and step-length is 1, and pond layer takes maximum pond mode, pond
Change window size is 2x2, and step-length is 2.The port number of convolutional layer and full articulamentum be respectively (32,64,128,256,500,
4096) characteristic vector of 4096 dimensions, is obtained.
In order to improve license board information recognition speed, neural network model is provided with multiple character result output layers simultaneously, many
Individual character result output layer corresponds to character identification result in output license plate image sample respectively.
Step S13 is specifically included：
Using the license board information in multiple license plate image samples and license plate image sample as training data, to neutral net mould
Type is trained, until each character result output layer of neural network model is to character recognition in license plate image sample license board information
The discrimination of result converges on preset value more than the penalty values of the first predetermined threshold value or the loss function of neural network model.Its
In middle license board information in the length recognition result and car plate of character identification result including license board information character recognition result；
Used as a kind of optional implementation method, the neural network model is provided with length sequences output layer, for exporting
Character length recognition result in the license plate image sample.
Specific training method is repeated no more with the character result training process of above-mentioned steps S12.
Specifically, the characteristic vector of 4096 dimensions of the present embodiment can set multiple output layers and be connected, and obtain multiple outputs
As a result, when the number of output layer can be set 6 output layers, for recognizing license plate image letter and digital information, or 7 defeated
Go out layer, Chinese character information and letter and digital information or 8 output layers for recognizing car plate, in the Chinese character of identification car plate
Information and letter and digital information, it is also possible to identify the length of car plate.Characters on license plate length is generally 7 or 8, because
This, character length output result is set to the classification tasks of CNN tri-, and the car plate digit of the first branching representation identification is 0, represents car plate
Identification mistake, the car plate digit of the second branching representation identification is 7, and the car plate digit of the 3rd branching representation identification is 8；Output
Layer is that first the 1st chinese character recognition result for branching into output car plate, chinese character is recognized to the output result of character
Branch's output result integer value (representing 34 region Chinese characters) between 0-34；Rear the 6 of second branch output car plate or 7 numbers
The recognition result of code, number-plate number identification branch output result is that arbitrary value between 0-36 (represents 0-9, A-Z totally 36 numeric words
Alphabetic character and false-trademark), when its output can be expressed as ZSi=WSiH+bSi, (i=1,2,3 ... 6,7,8), wherein W represents connection
Weights, bsi is biasing, wherein the convolutional neural networks model use classification corresponding to the output valve of maximum a posteriori probability for
Final output value, i.e.,：
S=(l, s1, s2, s3, s4, s5, s6, s7, s8)=arg maxL,S1,S2,S3,...,S8log P(S|X)
The method for setting up Car license recognition model provided in an embodiment of the present invention, by obtaining the license plate image of multiple vehicles,
License plate image is expanded to pre-set dimension, license plate image sample is obtained, and by multiple license plate image samples and license plate image sample
In license board information as training data, neural network model is trained, until neural network model is to license plate image sample
The discrimination of this license board information converges on pre- more than the penalty values of the first predetermined threshold value or the loss function of neural network model
If value, then carries out license board information identification using the neural network model for training to license plate image to be identified, solve existing
Licence plate recognition method does not adapt to environment complicated and changeable and identification process takes serious problem.
Embodiment 2
The embodiment of the present invention provides a kind of device for setting up Car license recognition model, as shown in Fig. 2 including：
Acquiring unit 21, the license plate image for obtaining multiple vehicles；
License plate image sample acquisition unit 22, for license plate image to be expanded into pre-set dimension, obtains license plate image sample；
Training unit 23, for using the license board information in multiple license plate image samples and license plate image sample as training number
According to being trained to neural network model, until neural network model is big to the discrimination of license board information in license plate image sample
Preset value is converged in the penalty values of the first predetermined threshold value or the loss function of neural network model.
Preferably, neural network model is convolutional neural networks model.
Preferably,
The neural network model is provided with multiple character result output layers, and the multiple character result output layer is right respectively
The recognition result of multiple characters in the license plate image sample should be exported；
The training unit, including：Training subelement, is used for
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to god
It is trained through network model, until multiple character result output layers of the neural network model are to more in license plate image sample
The discrimination of the recognition result of individual character is converged on more than the penalty values of predetermined threshold value or the loss function of neural network model
Preset value.
Preferably, the neural network model is provided with length sequences output layer, for exporting the license plate image sample
Middle character length recognition result.
Preferably, license plate image sample acquisition unit includes：
Coordinate and size acquiring unit, coordinate and size for obtaining license plate image；
Image center determining unit, for coordinate and size according to license plate image, determines the license plate image
Image center；
License plate image sample determining unit, for centered on image center, license plate image being carried out according to pre-set dimension
Extension, obtains license plate image sample.
The device for setting up Car license recognition model provided in an embodiment of the present invention, the car of multiple vehicles is obtained by acquiring unit
Board image, pre-set dimension is expanded to by license plate image, obtains license plate image sample, and by multiple license plate image samples and car plate figure
License board information in decent is trained as training data to neural network model, until neural network model is to car plate
The discrimination of image pattern license board information is received more than the penalty values of the first predetermined threshold value or the loss function of neural network model
Hold back in preset value, license board information identification is then carried out to license plate image to be identified using the neural network model for training, solve
Existing licence plate recognition method does not adapt to environment complicated and changeable and identification process takes serious problem.
Embodiment 3
The embodiment of the present invention provides a kind of license board information recognition methods, as shown in figure 3, including：
S31, obtains license plate image to be identified；
S32, license plate image to be identified is input in the model that method as described in Example 1 is set up, and is determined to be identified
The license board information of license plate image.For example, a license board information is capital AAB123, the 6th machine of character " 1 " trains identification
During, poll identification numeral 0-9, the maximum a posteriori probability for obtaining is respectively 0.001,0.001,0.99,0.001,0.001,
0.001st, 0.001,0.001,0.001,0.001,0.001,0.001, then when identifying that 0.99 corresponding digital 1 is car plate
6th is character, and the information of other characters of car plate is similarly obtained by the method for maximum a posteriori probability, and images to be recognized is each
The recognition result of position is 0.99,0.97,0.98,0.96,0.99,0.99,0.99,0.98,0.95, will then be obtained on each
Result even multiply, when even result is multiplied more than the second predetermined threshold value, then images to be recognized is license plate image, and the present embodiment is excellent
It is 0.3 to select the second predetermined threshold value；When the recognition result of a certain position is far smaller than the recognition result on other, can now draw
Images to be recognized identification mistake is recognized checking.Have in car plate length recognition result is 0 and character identification result
Output result more than 4 is 0, then judge that images to be recognized is false-trademark.
License board information recognition methods provided in an embodiment of the present invention, by the machine learning model that trains to car to be identified
Board image, improves the accuracy rate and recognition speed of license plate image information identification.
Embodiment 4
The embodiment of the present invention provides a kind of license board information identifying device, as shown in figure 4, including：
License plate image acquiring unit 41 to be identified, for obtaining license plate image to be identified；
License board information determining unit 42, for the license plate image to be identified to be input into method as described in Example 1
In the model of foundation, the license board information of license plate image to be identified is determined.
License board information identifying device provided in an embodiment of the present invention, by the machine learning model that trains to car to be identified
Board image, improves the accuracy rate and recognition speed of license plate image information identification.
Obviously, above-described embodiment is only intended to clearly illustrate example, and not to the restriction of implementation method.It is right
For those of ordinary skill in the art, can also make on the basis of the above description other multi-forms change or
Change.There is no need and unable to be exhaustive to all of implementation method.And the obvious change thus extended out or
Among changing still in the protection domain of the invention.
Claims (10)
1. a kind of method for setting up Car license recognition model, it is characterised in that including：
Obtain the license plate image of multiple vehicles；
The license plate image is expanded to pre-set dimension, license plate image sample is obtained；
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to nerve net
Network model is trained, until the neural network model is more than to the discrimination of the license plate image sample license board information presetting
The penalty values of the loss function of threshold value or neural network model converge on preset value.
2. method according to claim 1, it is characterised in that the neural network model is convolutional neural networks model.
3. method according to claim 1, it is characterised in that
The neural network model is provided with multiple character result output layers, and the multiple character result output layer corresponds to defeated respectively
Go out the recognition result of multiple characters in the license plate image sample；
The license board information using in the multiple license plate image sample and the license plate image sample as training data, to god
It is trained through network model, until the neural network model is more than in advance to the discrimination of license board information in license plate image sample
If the penalty values of the loss function of threshold value or neural network model converge on preset value, including：
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to nerve net
Network model is trained, until multiple character result output layers of the neural network model are to multiple words in license plate image sample
The discrimination of the recognition result of symbol converges on default more than the penalty values of predetermined threshold value or the loss function of neural network model
Value.
4. method according to claim 3, it is characterised in that the neural network model is provided with length sequences output
Layer, for exporting character length recognition result in the license plate image sample.
5. method according to claim 1, it is characterised in that described that the license plate image is expanded into pre-set dimension, obtains
To license plate image sample, including：
Obtain the coordinate and size of the license plate image；
According to the coordinate and size of the license plate image, the image center of the license plate image is determined；
Centered on described image central point, the license plate image extension is carried out according to the pre-set dimension, obtain the car plate
Image pattern.
6. a kind of device for setting up Car license recognition model, it is characterised in that including：
Acquiring unit, the license plate image for obtaining multiple vehicles；
License plate image sample acquisition unit, for the license plate image to be expanded into pre-set dimension, obtains license plate image sample；
Training unit, for using the license board information in the multiple license plate image sample and the license plate image sample as training
Data, are trained to neural network model, until the neural network model is to license board information in the license plate image sample
Discrimination converge on preset value more than the penalty values of the loss function of predetermined threshold value or neural network model.
7. device according to claim 6, it is characterised in that the neural network model is convolutional neural networks model.
8. device according to claim 6, it is characterised in that
The neural network model is provided with multiple character result output layers, and the multiple character result output layer corresponds to defeated respectively
Go out the recognition result of multiple characters in the license plate image sample；
The training unit, including：
Training subelement, is used for
Using the license board information in the multiple license plate image sample and the license plate image sample as training data, to nerve net
Network model is trained, until multiple character result output layers of the neural network model are to multiple words in license plate image sample
The discrimination of the recognition result of symbol converges on default more than the penalty values of predetermined threshold value or the loss function of neural network model
Value.
9. device according to claim 8, it is characterised in that the neural network model is provided with length sequences output
Layer, for exporting character length recognition result in the license plate image sample.
10. device according to claim 6, it is characterised in that the license plate image sample acquisition unit includes：
Coordinate and size acquiring unit, coordinate and size for obtaining the license plate image；
Image center determining unit, for coordinate and size according to the license plate image, determines the license plate image
Image center；
License plate image sample determining unit, for centered on described image central point, being carried out according to the pre-set dimension described
License plate image extends, and obtains the license plate image sample.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220675A (en) * | 2017-06-21 | 2017-09-29 | 苏州卡睿知光电科技有限公司 | A kind of method and device for setting up eyeglass identification model and eyeglass identification |
| CN107330878A (en) * | 2017-06-21 | 2017-11-07 | 苏州卡睿知光电科技有限公司 | A kind of method and device for setting up eyeglass identification model |
| CN108334881A (en) * | 2018-03-12 | 2018-07-27 | 南京云创大数据科技股份有限公司 | A kind of licence plate recognition method based on deep learning |
| CN108510084A (en) * | 2018-04-04 | 2018-09-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| WO2019175686A1 (en) | 2018-03-12 | 2019-09-19 | Ratti Jayant | On-demand artificial intelligence and roadway stewardship system |
| CN110443245A (en) * | 2019-08-14 | 2019-11-12 | 上海世茂物联网科技有限公司 | Localization method, device and the equipment of a kind of license plate area under unrestricted scene |
| CN111046891A (en) * | 2018-10-11 | 2020-04-21 | 杭州海康威视数字技术股份有限公司 | Training method of license plate recognition model, and license plate recognition method and device |
| CN112580643A (en) * | 2020-12-09 | 2021-03-30 | 浙江智慧视频安防创新中心有限公司 | License plate recognition method and device based on deep learning and storage medium |
| CN112801093A (en) * | 2021-01-11 | 2021-05-14 | 爱泊车美好科技有限公司 | License plate information calibration method and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100278436A1 (en) * | 2009-04-30 | 2010-11-04 | Industrial Technology Research Institute | Method and system for image identification and identification result output |
| CN104298976A (en) * | 2014-10-16 | 2015-01-21 | 电子科技大学 | License plate detection method based on convolutional neural network |
-
2017
- 2017-01-19 CN CN201710039025.6A patent/CN106874907B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100278436A1 (en) * | 2009-04-30 | 2010-11-04 | Industrial Technology Research Institute | Method and system for image identification and identification result output |
| CN104298976A (en) * | 2014-10-16 | 2015-01-21 | 电子科技大学 | License plate detection method based on convolutional neural network |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220675A (en) * | 2017-06-21 | 2017-09-29 | 苏州卡睿知光电科技有限公司 | A kind of method and device for setting up eyeglass identification model and eyeglass identification |
| CN107330878A (en) * | 2017-06-21 | 2017-11-07 | 苏州卡睿知光电科技有限公司 | A kind of method and device for setting up eyeglass identification model |
| CN108334881A (en) * | 2018-03-12 | 2018-07-27 | 南京云创大数据科技股份有限公司 | A kind of licence plate recognition method based on deep learning |
| WO2019175686A1 (en) | 2018-03-12 | 2019-09-19 | Ratti Jayant | On-demand artificial intelligence and roadway stewardship system |
| CN108334881B (en) * | 2018-03-12 | 2022-04-29 | 南京云创大数据科技股份有限公司 | License plate recognition method based on deep learning |
| CN108510084A (en) * | 2018-04-04 | 2018-09-07 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN111046891A (en) * | 2018-10-11 | 2020-04-21 | 杭州海康威视数字技术股份有限公司 | Training method of license plate recognition model, and license plate recognition method and device |
| CN110443245A (en) * | 2019-08-14 | 2019-11-12 | 上海世茂物联网科技有限公司 | Localization method, device and the equipment of a kind of license plate area under unrestricted scene |
| CN110443245B (en) * | 2019-08-14 | 2022-02-15 | 上海世茂物联网科技有限公司 | License plate region positioning method, device and equipment in non-limited scene |
| CN112580643A (en) * | 2020-12-09 | 2021-03-30 | 浙江智慧视频安防创新中心有限公司 | License plate recognition method and device based on deep learning and storage medium |
| CN112801093A (en) * | 2021-01-11 | 2021-05-14 | 爱泊车美好科技有限公司 | License plate information calibration method and device |
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